Staff Machine Learning Engineer (consumer Team), Hyderabad

Warner Bros Discovery Warner Bros Discovery · Media · Hyderabad, Telangāna, India · Technology

Staff Machine Learning Engineer at Warner Bros. Discovery, Hyderabad, focusing on consumer platforms. The role involves technical leadership in designing and operating low-latency online serving systems, building ML models for identity resolution, audience intelligence, content affinity, and forecasting. Responsibilities include architecting probabilistic identity resolution systems, leading Audience Intelligence evolution, owning forecasting ML architecture, and integrating personalization signals. The role also emphasizes experimentation, quality, observability, and mentorship, with a focus on agentic AI development workflows and LLM-augmented pipelines.

What you'd actually do

  1. Design and operate low latency online serving systems for fraud scoring, message decisioning and real-time personalization
  2. Build ML models for identity resolution, audience intelligence, content affinity modeling, genre-preference modeling and time-series forecasting across global markets
  3. Design feature pipelines that fuse real-time streaming signals with batch-computed features for online scoring
  4. Architect probabilistic identity resolution systems that connect unauthenticated device IDs and first-party cookies to households/persons with calibrated confidence across WBD brands.
  5. Lead the evolution of Audience Intelligence, including ML Promo Optimizer, STAT v2, lookalike modeling inside Snowflake DCR, and content segmentation.

Skills

Required

  • Machine Learning Engineering
  • Python
  • SQL
  • MLOps
  • Data Engineering
  • Cloud Platforms (AWS)
  • Databricks
  • Snowflake
  • Low-latency serving systems
  • Identity Resolution
  • Audience Modeling
  • Content Affinity Modeling
  • Time-series Forecasting
  • Feature Engineering
  • Experiment Design
  • Model Evaluation
  • Observability
  • Agentic AI
  • LLM-augmented pipelines

Nice to have

  • Graph ML
  • DCR-native modeling
  • ML Promo Optimizer
  • STAT v2
  • lookalike modeling
  • content segmentation

What the JD emphasized

  • agentic AI development workflows
  • LLM-augmented pipelines
  • probabilistic identity resolution
  • Audience Intelligence
  • forecasting

Other signals

  • design and operate low latency online serving systems
  • build ML models for identity resolution, audience intelligence, content affinity modeling, genre-preference modeling and time-series forecasting
  • design feature pipelines that fuse real-time streaming signals with batch-computed features for online scoring
  • architect probabilistic identity resolution systems
  • lead the evolution of Audience Intelligence
  • own ML architecture for forecasting use cases
  • bring ML personalization signals into batch and future real-time activation paths
  • design offline and online evaluation approaches
  • improve feature and label quality, leakage prevention, bias checks, calibration, explainability
  • turn incidents and postmortems into reusable standards, automated checks, and platform improvements
  • mentor Senior and MLE 2 engineers
  • partner with the ML Engineering Manager on technical roadmap shaping
  • create clear design docs, architecture reviews, readiness reviews, and postmortems